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DS 4400 - Machine Learning and Data Mining 1 |

Introduces supervised and unsupervised predictive modeling, data mining, and machine-learning concepts. Uses tools and libraries to analyze data sets, build predictive models, and evaluate the fit of the models. Covers common learning algorithms, including dimensionality reduction, classification, principal-component analysis, k-NN, k-means clustering, gradient descent, regression, logistic regression, regularization, multiclass data and algorithms, boosting, and decision trees. Studies computational aspects of probability, statistics, and linear algebra that support algorithms, including sampling theory and computational learning. Requires programming in R and Python. Applies concepts to common problem domains, including recommendation systems, fraud detection, or advertising. Prereq. (a) DS 4300 and (b) ECON 2350, ENVR 2500, MATH 3081, or PSYC 2320.
4.000 Credit hours 4.000 Lecture hours Levels: Undergraduate Schedule Types: Lecture Data Science Department Course Attributes: NUpath Analyzing/Using Data, Computer&Info Sci Restrictions: Must be enrolled in one of the following Levels: Undergraduate Prerequisites: Undergraduate level DS 4300 Minimum Grade of D- and (Undergraduate level ECON 2350 Minimum Grade of D- or Undergraduate level ENVR 2500 Minimum Grade of D- or Undergraduate level MATH 3081 Minimum Grade of D- or Undergraduate level PSYC 2320 Minimum Grade of D-) |

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